{"id":3827384,"url":"https://alion.io/job/hcltech-senior-technical-specialist-185","title":"Senior Technical Specialist","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Sales","role_family":"Sales","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure 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deliver scalable ML solutions.\nMust-Have Skills\nMinimum 6 years of experience in ML Engineering or MLOps.\nStrong proficiency in Python and SQL.\nExperience with Apache Spark or similar frameworks for large-scale data processing.\nHands-on experience with CI/CD tools (e.g., Azure DevOps).\nFamiliarity with MLOps frameworks, model registries, and experiment tracking.\nProficient in Git for version control.\nNice-to-Have Skills\nExperience with Docker, Kubernetes, and ML monitoring tools.\nSolid understanding of ML algorithms (e.g., regression, classification, clustering).\nFamiliarity with libraries such as scikit-learn and pandas.\nAbility to translate business requirements into technical solutions.\nSoft Skills\nStrong customer focus and stakeholder engagement.\nAbility to align technical deliverables with business objectives.\nKey Responsibilities\nKey Responsibilities\nDesign and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.\nProductionize ML models into clean, modular, and maintainable codebases.\nImplement versioning and governance practices for data, code, and models.\nMonitor model performance, data drift, and system reliability.\nCollaborate with cross-functional teams to deliver scalable ML solutions.\nMust-Have Skills\nMinimum 6 years of experience in ML Engineering or MLOps.\nStrong proficiency in Python and SQL.\nExperience with Apache Spark or similar frameworks for large-scale data processing.\nHands-on experience with CI/CD tools (e.g., Azure DevOps).\nFamiliarity with MLOps frameworks, model registries, and experiment tracking.\nProficient in Git for version control.\nNice-to-Have Skills\nExperience with Docker, Kubernetes, and ML monitoring tools.\nSolid understanding of ML algorithms (e.g., regression, classification, clustering).\nFamiliarity with libraries such as scikit-learn and pandas.\nAbility to translate business requirements into technical solutions.\nSoft Skills\nStrong customer focus and stakeholder engagement.\nAbility to align technical deliverables with business objectives.\nSkill Requirements\nKey Responsibilities\nDesign and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.\nProductionize ML models into clean, modular, and maintainable codebases.\nImplement versioning and governance practices for data, code, and models.\nMonitor model performance, data drift, and system reliability.\nCollaborate with cross-functional teams to deliver scalable ML solutions.\nMust-Have Skills\nMinimum 6 years of experience in ML Engineering or MLOps.\nStrong proficiency in Python and SQL.\nExperience with Apache Spark or similar frameworks for large-scale data processing.\nHands-on experience with CI/CD tools (e.g., Azure DevOps).\nFamiliarity with MLOps frameworks, model registries, and experiment tracking.\nProficient in Git for version control.\nNice-to-Have Skills\nExperience with Docker, Kubernetes, and ML monitoring tools.\nSolid understanding of ML algorithms (e.g., regression, classification, clustering).\nFamiliarity with libraries such as scikit-learn and pandas.\nAbility to translate business requirements into technical solutions.\nSoft Skills\nStrong customer focus and stakeholder engagement.\nAbility to align technical deliverables with business objectives.\nOther Requirements\nKey Responsibilities\nDesign and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.\nProductionize ML models into clean, modular, and maintainable codebases.\nImplement versioning and governance practices for data, code, and models.\nMonitor model performance, data drift, and system reliability.\nCollaborate with cross-functional teams to deliver scalable ML solutions.\nMust-Have Skills\nMinimum 6 years of experience in ML Engineering or MLOps.\nStrong proficiency in Python and SQL.\nExperience with Apache Spark or similar frameworks for large-scale data processing.\nHands-on experience with CI/CD tools (e.g., Azure DevOps).\nFamiliarity with MLOps frameworks, model registries, and experiment tracking.\nProficient in Git for version control.\nNice-to-Have Skills\nExperience with Docker, Kubernetes, and ML monitoring tools.\nSolid understanding of ML algorithms (e.g., regression, classification, clustering).\nFamiliarity with libraries such as scikit-learn and pandas.\nAbility to translate business requirements into technical solutions.\nSoft Skills\nStrong customer focus and stakeholder engagement.\nAbility to align technical deliverables with business objectives.","description_format":"text","description_chars":4866,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Managed IT 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